Innovative approaches in soil carbon sequestration modelling for better prediction with limited data.

ABSTRACT: Soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the Paris and Kyoto protocol agreements. Land managers typically rely on computationally complex models fit using sparse datasets to make these accounts and predictions. The model complexity and sparsity of the data can lead to over-fitting, leading to inaccurate results when making predictions with new data. Modellers address over-fitting by simplifying their models and reducing the number of parameters, and in the current context this could involve neglecting some soil organic carbon (SOC) components. In this study, we introduce two novel SOC models and a new RothC-like model and investigate how the SOC components and complexity of the SOC

SUBMITTER: Davoudabadi MJ 

PROVIDER: S-EPMC10850547 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature